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Several concepts that model processes of spreading (of information, disease, objects, etc.) in graphs or networks have been studied. In many contexts, we assume that some vertices of a graph G are contaminated initially, before the process starts. By the q -forcing rule, a contaminated vertex having at most q uncontaminated neighbors enforces all the neighbors to become contaminated, while by the p -percolation rule, an uncontaminated vertex becomes contaminated if at least p of its neighbors are contaminated. If given a set S of initially contaminated vertices all vertices eventually become contaminated when continuously applying the q -forcing rule (respectively the p -percolation rule), S is called a q -forcing set (respectively, a p -percolating set) in G. In this paper, we consider sets S that are at the same time q -forcing sets and p -percolating sets, and call them (p , q) -spreading sets. Given positive integers p and q , the minimum cardinality of a (p , q) -spreading set in G is a (p , q) -spreading number, σ (p , q) (G) , of G. While q -forcing sets have been studied in a dozen of papers, the decision version of the corresponding graph invariant has not been considered earlier, and we fill the gap by proving its NP-completeness. This, in turn, enables us to prove the NP-completeness of the decision version of the (p , q) -spreading number in graphs for an arbitrary choice of p and q. Again, for every p ∈ N and q ∈ N ∪ { ∞ } , we find a linear-time algorithm for determining the (p , q) -spreading number of a tree, where in the case p ≥ 2 we apply Riedl's algorithm from [Largest and smallest minimal percolating sets in trees, Electron. J. Combin. 19 (2012) Paper 64] on p -percolation in trees. In addition, we present a lower and an upper bound on the (p , q) -spreading number of a tree and characterize extremal families of trees. In the case of square grids, we combine some results of Bollobás from [The Art of Mathematics: Coffee Time in Memphis. Cambridge Univ. Press, New York, 2006], and the AIM Minimum Rank-Special Graphs Work Group from [Zero forcing sets and the minimum rank of graphs, Linear algebra Appl. 428 2008 1628–1648], and new results on (2 , 1) -spreading and (4 , q) -spreading to obtain σ (p , q) (P m □ P n) for all (p , q) ∈ (N ∖ { 3 }) × (N ∪ { ∞ }) and all m , n ∈ N. [ABSTRACT FROM AUTHOR]

• A pipeline is formulated to extract window to wall ratios from building façades. • A grammar based and learning based method are compared for performance efficacy. • The learning-based method shows higher accuracy than the grammar-based method. • A hybrid method combining the strengths of the methods achieves higher accuracy. • An algorithm utilizing the learning-based method extracts detailed building heights. Urban feature extraction methods have presented multiple opportunities in the field of environmental building performance studies, and urban assessments. With reference to façade features, window size and position are key parameters that influence occupant perception and environmental performance of indoor spaces. A myriad of methods have been proposed to automatically detect façade window layouts from street view images yet it is still unclear how to assess the strengths and limitations of these methods or how they can be combined for higher detection performance. This paper aims to add clarity for those aiming to use computer vision techniques for façade layout extraction for urban level sustainability assessments. An automated pipeline to enable the extraction and detection of WWRs (Window to Wall ratios) is introduced that is based on two fundamentally different computational approaches; a grammar-based edge detection framework (Method 1) and a learning-based method (Method 2) that utilizes CNNs (Convolutional Neural Networks). The paper then compares the detection efficacy of both methods, focusing on WWR accuracy, in New York and Lisbon, including their ability to extract more detailed façade properties such as floor-to-floor heights. The study finds that the learning-based method shows lower error scores across the two cities. In Lisbon, 69 % of conditions were detected within the 10 % error range and 91 % were within the 20 % range under Method 1. Under Method 2, 82.5 % of conditions were within the 10 % error range and 95 % were within the 20 % range. In New York, 66 % of conditions were within the 10 % error range and 90 % were within the 20 % range under Method 1 while 77.5 % of conditions were within the 10 % error range and 93 % were within the 20 % range under Method 2. Finally, a hybrid method is proposed to leverage the strengths of the two models, and higher accuracies are obtained in both the New York and Lisbon dataset. In New York, 96.5 % are now detected within the 20 % error range and 81.5 % within the 10 % error range. In Lisbon, 96 % are now detected within the 20 % error range and 83.5 % within the 10 % error range. With reference to the total building height extraction formulated under Method 2, the results show a relative error of 3.5 % in height estimation in the sample set. [ABSTRACT FROM AUTHOR]

Climate change is altering the context of human mobility due to disasters such as sea-level rise, forest fires, and other extreme events. It is also changing how places plan and adapt to these chaotic events. In this paper we examine how climate destinations, or host communities, are preparing for potential climate related in-migration in their climate adaptation planning. While no place will be left untouched by climate change, a number of cities and communities across Upstate New York consider themselves "climate havens" due to their lower risk to many climate impacts, such as sea level rise, access to freshwater resources, and infrastructure to accommodate growth. Within this context, what does it mean to plan a just climate haven? What types of policy and planning might be needed? How are planners and practitioners framing adaptation approaches? Using Q-methodology, we reveal two dominant perspectives among planners and activists in Upstate New York on how to plan just climate havens. The first is a transformational view that looks to address the social, political, and economic drivers of vulnerability. The second is a recognition view that looks to address legal and legislative shortcomings in how to support climate migrants. Points of consensus on how to build just climate havens between the two groups include co-production and co-design, workforce development, food security, and safe and affordable housing. We suggest that these two perspectives frame justice in two distinct—but overlapping—ways and provide a shared basis on how to consider planning a just climate haven. • Two perspectives frame how to plan just climate havens: Transformation and Recognition. • Perspectives unite around workforce development, food security, housing, and co-produced solutions, and material gains. • Climate destinations in Central New York need to build capacity to plan and adapt to future climate scenarios. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic and consequential shutdown measures, many mental health professionals started providing therapy to patients exclusively via telehealth. Our research center, which specializes in studying and treating obsessive-compulsive disorder (OCD), historically has provided in-person exposure and response prevention (ERP) to adults with OCD, but shifted to telehealth during the pandemic. Unlike in other modes of talk therapy, ERP's emphasis on therapist-supervised exposures presented unique opportunities and challenges to delivering treatment entirely via a virtual platform. This paper provides case examples to illustrate lessons we learned delivering ERP exclusively via telehealth in New York from March 2020 through June 2021 and offers recommendations for future study and practice. Though we observed a number of drawbacks to fully remote ERP, we also discovered advantages to delivering ERP this way, meriting additional research attention. [ABSTRACT FROM AUTHOR]

• The lives of Black girls, femmes, transgender, and nonbinary (TGNB) young people who identify as LGBTQ + are punctuated by experiences of oppression-based, interpersonal, and systemic trauma. • Healing and creative resistance are strong driving forces facilitated by pride in one's identities, artistic expression, and mindfulness facilitated by time spent in nature and parks. • Individual and collective dreaming and futuring are central to the living of LGBTQ + identified Black girls, femmes, and TGNB young people. Black LGBTQ + girls, femmes, transgender and nonbinary youth and young adults experience high rates of sexual violence, and other forms of trauma, including oppression-based trauma related to racism, misogynoir, homophobia and transphobia. Black LGBTQ + girls, femmes, transgender and nonbinary young people, whose experiences are underexplored within social science research, also experience disparately harsh discipline and pushout within school settings while being disproportionately represented in the foster care and juvenile justice systems. However, they are also a community with a tremendous history of creative resistance, and legacies of activism and powerful healing practices. Within this paper we have disaggregated the interview data of 13 LGBTQ + identified participants from a larger qualitative study with 24 Black girls, femmes, transgender and nonbinary youth, aged 16–25, who reside in New York State. Data were analyzed using critical thematic analysis and three main themes emerged: 1) the significance of participants' experiences of childhood trauma, 2) participants' conceptualizations and experiences of healing and resistance, and 3) participants aspirations and dreams for their futures and the futures of their communities. [ABSTRACT FROM AUTHOR]

• Frontline communities are often excluded and tokenized in coastal resiliency planning. • Interviews with New York-area community leaders reveal pathways to better planning. • Centering and investing in community-level expertise can improve planning outcomes. • Equitable partnerships require ongoing dialogue, accountability and self-evaluation. • Iterative co-production of resiliency research yields valuable, actionable insight. Community-based organizations (CBOs) in frontline coastal communities grapple with social and environmental injustices compounded by climate change risks. In response, CBOs have developed deep expertise in climate adaptation tailored to their local communities. Yet these groups are often effectively excluded from resilience planning processes that are top-down and involve perfunctory and often performative consultations. This paper asks: What do community leaders seek from adaptation planning, and how do they recommend such processes be improved? Drawing on the experiences of ten CBOs in coastal New York and New Jersey, the majority representing BIPOC environmental justice communities, this article advances community-driven priorities for coastal resilience planning outcomes and processes. We conducted structured 60–90-minute interviews with ten CBO leaders between February-March 2022, collaboratively completed an iterative content analysis of the interview data and community plans, and workshopped core findings in multiple sessions and conversations with participating CBOs through early 2024. CBO leaders had consensus on resilience planning priorities: they oppose top-down approaches where planners bring a predetermined agenda, and seek true partnership through a relational approach that values grassroots perspectives to co-produce equitable and just strategies to address climate risk. Recommendations for decision-makers center on the need to build on existing community-led plans, invest in community leadership within planning processes, act with transparency to foster trust, partnership and co-planning with communities, and self-evaluate their practice. Lessons for researchers seeking to support community leadership within resilience planning include the need to establish lasting and mutually supportive relationships with community partners to enable knowledge co-production. [ABSTRACT FROM AUTHOR]

Urban mobility services face the challenge of planning their operations efficiently while complying with user preferences. In this paper, we introduce a new mathematical model called a choice-driven dial-a-ride problem (CD-DARP) which is a generalization of the dynamic DARP where passenger behavior is integrated in the operational planning using choice models and assortment optimization. We look at two types of mobility services, private and shared. Our problem extends the dynamic DARP by (i) changing its objective function to profit maximization, where both cost and revenue are variables, and (ii) incorporating assortment optimization with routing decisions in a dynamic setting. We propose a pricing scheme based on a choice model designed to offer service alternatives at the time a customer makes a request. We introduce a tailored algorithm to efficiently solve the dynamic CD-DARP. Computational results indicate that our proposed approach outperforms dynamic DARP in terms of reducing routing costs and improving the number of customers served. • A new framework is introduced for DDARP incorporating customer choice. • The routing cost is reduced while satisfying customers' requests. • CD-DARP provides more flexibility for the operator to efficiently manage the fleet. • We evaluate the proposed model on synthetic as well as New York taxi data. • Flexible time windows helps improving the service level. [ABSTRACT FROM AUTHOR]

• Propose a duopoly evolutionary game model for the dynamic ride-hailing market. • Capture bounded rationality, strategic learning, and dynamic competition between platforms. • Identify the existence of stationary points and derive simplified stability conditions. • Establish two extended models to capture the service aggregator and mixed electric/fuel fleets. • Provide a fresh perspective for exploring dynamic ride-hailing competition and market evolution. The platform competition in a real-world ride-hailing market is complex and dynamic. Existing research mainly relies on static competitive equilibrium, lacking an understanding of the dynamic evolution process of platform competition. This paper develops a generic duopoly evolutionary game model to capture dynamic decision adjustments and the evolution of market conditions. Bounded rationality and strategic learning are integrated into the modeling to dissect the interaction and competition between two platforms. The existence of stationary points where the system states remain unchanged over time is identified, and simplified stability conditions are derived. A pure strategy pair is found stable when constituting a strict Nash equilibrium and corresponding to positive payoffs. We establish two extended models to capture the nascent service aggregator and mixed electric/fuel fleets and compare them with the basic model. The model is calibrated employing the ride-hailing data of New York and is applicable for analyzing the market evolution under different states. The impacts of potential demand drop, aggregated services, and mixed fleets on platform decision and driver-passenger choice evolution are unveiled. It is found that the platforms possess self-recovery capability when facing a deteriorated external environment. The decision-making of the small platform exhibits a more substantial inertia and volatility since the large platform's decision can easily influence the market supply and demand. In the initial stage, the service aggregator slightly harms the large platform, but in the long run, it benefits both platforms. Besides, both platforms reduce prices while inflating commission ratios under mixed fleets. This study provides a fresh perspective for exploring the real-world dynamic ride-hailing competition and market evolution. [ABSTRACT FROM AUTHOR]

The U.S. opioid overdose crisis persists. Outpatient behavioral health services (BHS) are essential components of a comprehensive response to opioid use disorder and overdose fatalities. The Helping to End Addiction Long-Term® (HEALing) Communities Study developed the Communities That HEAL (CTH) intervention to reduce opioid overdose deaths in 67 communities in Kentucky, Ohio, New York, and Massachusetts through the implementation of evidence-based practices (EBPs), including BHS. This paper compares the rate of individuals receiving outpatient BHS in Wave 1 intervention communities (n = 34) to waitlisted Wave 2 communities (n = 33). Medicaid data included individuals ≥18 years of age receiving any of five BHS categories: intensive outpatient, outpatient, case management, peer support, and case management or peer support. Negative binomial regression models estimated the rate of receiving each BHS for Wave 1 and Wave 2. Effect modification analyses evaluated changes in the effect of the CTH intervention between Wave 1 and Wave 2 by research site, rurality, age, sex, and race/ethnicity. No significant differences were detected between intervention and waitlisted communities in the rate of individuals receiving any of the five BHS categories. None of the interaction effects used to test the effect modification were significant. Several factors should be considered when interpreting results—no significant intervention effects were observed through Medicaid claims data, the best available data source but limited in terms of capturing individuals reached by the intervention. Also, the 12-month evaluation window may have been too brief to see improved outcomes considering the time required to stand-up BHS. Clinical Trials.gov http://www.clinicaltrials.gov : Identifier: NCT04111939 • Evidence-based practices to reduce opioid overdose deaths implemented in 67 communities. • Outpatient behavioral health services are key element of opioid addiction treatment. • Similar receipt of behavioral services in control and intervention communities. • More research needed to test the impact of comprehensive, community-driven interventions. [ABSTRACT FROM AUTHOR]

We evaluated caretaker knowledge of and attitudes toward the levonorgestrel-containing intrauterine device (L-IUD) as a treatment option for adolescents and young adults with heavy menstrual bleeding. A survey assessed demographic characteristics, menstrual history, perception of the L-IUD, and knowledge of the indications, risks, and benefits of the L-IUD. Participant recruitment occurred in the offices of a pediatric gynecologist, pediatric hematologist, and adolescent medicine physician in a hospital-based practice in New York. English-speaking caretakers of patients with a chief concern of heavy menstrual bleeding (<21 years old) were invited to participate. Electronic and paper surveys were administered prior to seeing the physician. The primary outcome was participant willingness to allow their daughter to use the L-IUD for heavy menstrual bleeding. Additional outcomes were explored by calculating a mean knowledge score and conducting a descriptive analysis of the perception questions. Forty surveys were included. Seventy percent of participants said they would allow their daughter to use birth control pills for heavy menstrual bleeding, whereas only 10% said the same of the L-IUD. The mean knowledge score was 73.3% (±15.7). Sixty-five percent of participants expressed the need for additional information on the L-IUD. Caretaker acceptance of the L-IUD as treatment for their daughter with heavy menstrual bleeding is limited. Although baseline knowledge was high, many indicated needing additional information. Because heavy menstrual bleeding can negatively impact quality of life, potential caretaker bias must be addressed to ensure full access to this highly effective treatment option. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had significant effects on urban and regional food systems. Local administrations worldwide have been challenged to design and implement policies to mitigate immediate food system disruptions while planning for longer-term equity and resilience. The fast pace and high degree of uncertainty of the pandemic have made systematic tracking and assessment of food system change and related policy responses arduous. To address this gap, this paper applies the multilevel perspective on sociotechnical transitions and the multiple streams framework on policy change to 16 months of food policy (March 2020 through June 2021) during the New York State-issued COVID-19 state of emergency, comprising more than 300 food policies advanced by New York City and State legislators and administrators. Content analysis of these policies revealed the most salient policy areas during this period, the status of legislation, and key programs and budget allocations, as well as local food governance and the organizational spaces within which food policy operates. The paper shows that food policy domains that gained prominence focused on support for food businesses and food workers and on ensuring and expanding food access through food security and nutrition policies. Most COVID-19 food policies were incremental and were limited to the duration of the emergency, yet the crisis allowed for enactment of novel policies that deviated from the common policy issues or the typical scale of changes proposed pre-pandemic. Taken together, and viewed through a multilevel and policy streams framework, the findings provide insight into the trajectory of food policymaking in New York during the pandemic and the areas that food justice activists, researchers, and policy makers should focus on as the COVID-19 pandemic is abated. • NY City and State advanced more than 300 food policies during the COVID emergency. • Policies focusing on food businesses, food workers, and food access were prominent. • Most policies were incremental and limited to the duration of the emergency. • The crisis propelled the enactment of several breakthrough food policies. • Pandemic food policymaking continues beyond the emergency. [ABSTRACT FROM AUTHOR]

• First study to report role of "bag bans" on retail return polyethylene contamination. • 1.4 – 2.8x increase in post-"bag ban" contamination from concentration effect. • 43-category contamination speciation increases understanding of the material stream. Plastic films are abundant, but seldom recycled in the United States (US). It is both economically and operationally challenging to recycle plastic films because their light weight makes accumulating material difficult. Additionally, films are not allowed in most curbside recycling programs in the US because they tangle around equipment used at material recovery facilities (MRFs) and effect a MRFs' ability to effectively sort other materials. As a result, the only recycling pathway for post-consumer plastic films in the US is retail return collection programs. Currently, only the most abundant type of post-consumer plastic film, polyethylene (PE), is accepted by these programs in the US. Although PE films come in many forms, grocery bags are the most well-known, and therefore most abundant, film in the retail return recycling stream. The New York State (NYS) Plastic Bag Waste Reduction Law prohibits the distribution of single use plastic bags. This work explored how ending the distribution of grocery bags impacted retail return recycling programs for PE films in Western New York. We show that a loss of the most recognizable and abundant PE film results in a 1.4 – 2.8x increase in contamination rates, devaluing films that are still collected. As a result, increased contamination rates may threaten the viability of this recycling pathway. The study also provides a complete speciation of this recycling stream with a novel level of detail, as existing research on this recycling pathway is limited. [ABSTRACT FROM AUTHOR]

• Marginalized families are disproportionately affected by COVID-19 pandemic. • Clinical vignettes describe integration of evidence-based practice and advocacy. • Child-focused clinicians can support marginalized communities as clinician-advocates. The novel coronavirus (COVID-19) has disproportionately impacted the health and socioeconomic outcomes for low-income populations, people of color, and immigrant children and families in the United States. As inequities in resources (i.e., food, internet, housing), health care, and education increased for marginalized families as a result of COVID-19, child-focused clinicians had to broaden their professional scope and implement new advocacy efforts. The current paper uses clinical vignettes taken from a New York State Office of Mental Health–licensed child and adolescent outpatient clinic in the Bronx, New York. The vignettes highlight the social inequities that impacted marginalized children and families during the pandemic, as well as the clinical team's response through the integration of evidence-base practice and advocacy. Implications for practice with vulnerable populations as the COVID-19 pandemic persists are discussed. [ABSTRACT FROM AUTHOR]

In federations such as the United States, governments at various levels are experimenting with new watershed governance arrangements to protect water quality for both ecosystem health and human consumption. Such arrangements may bring previously uncooperative governments together to credibly commit to resource protection under the auspices of new and intricate formal institutions. Given the risks of cooperation, theory indicates that a robust arrangement will contain means of holding governing actors accountable to each other. This paper examines a purportedly successful case, the New York City watershed governance arrangement, to identify how safeguards against intergovernmental opportunism promote lasting cooperation. Using the qualitative method of process tracing, this paper finds that the New York City watershed governance arrangement uses structural, judicial, and popular safeguards against opportunistic behaviors by governing actors that might threaten the resource or the arrangement. The results indicate that such safeguards are present and interact with other safeguards and rule institutions at the state and federal level to maintain compliance. [ABSTRACT FROM AUTHOR]

This paper examines overdose prevention programs based on peer administration of the opioid antagonist naloxone. The data for this study consist of 40 interviews and participant observation of 10 overdose prevention training sessions at harm reduction agencies in the Bronx, New York, conducted between 2010 and 2012. This paper contends that the social logic of peer administration is as central to the success of overdose prevention as is naloxone's pharmacological potency. Whereas prohibitionist drug policies seek to isolate drug users from the spaces and cultures of drug use, harm reduction strategies like peer-administered naloxone treat the social contexts of drug use as crucial resources for intervention. Such programs utilize the expertise, experience, and social connections gained by users in their careers as users. In revaluing the experience of drug users, naloxone facilitates a number of harm reduction goals. But it also raises complex questions about responsibility and risk. This paper concludes with a discussion of how naloxone's social logic illustrates the contradictions within broader neoliberal trends in social policy. [ABSTRACT FROM AUTHOR]

The Helping End Addictions Long Term (HEALing) Communities Study (HCS) seeks to significantly reduce overdose deaths in 67 highly impacted communities in Kentucky (KY), Massachusetts (MA), New York (NY), and Ohio (OH) by implementing evidence-based practices (EBPs) to reduce overdose deaths. The Opioid-overdose Reduction Continuum of Care Approach (ORCCA) organizes EBP strategies under three menus: Overdose Education and Naloxone Distribution (OEND), Medication Treatment for Opioid Use Disorder (MOUD), and Safer Prescribing and Dispensing Practices (SPDP). The ORCCA sets requirements for strategy selection but allows flexibility to address community needs. This paper describes and compiles strategy selection and examines two hypotheses: 1) OEND selections will differ significantly between communities with higher versus lower opioid-involved overdose deaths; 2) MOUD selections will differ significantly between urban versus rural settings. Wave 1 communities (n = 33) provided data on EBP strategy selections. Selections were recorded as a combination of EBP menu, sector (behavioral health, criminal justice, and healthcare), and venue (e.g., jail, drug court, etc.); target medication(s) were recorded for MOUD strategies. Strategy counts and proportions were calculated overall and by site (KY, MA, NY, OH), setting (rural/urban), and opioid-involved overdose deaths (high/low). Strategy selection exceeded ORCCA requirements across all 33 communities, with OEND strategies accounting for more (40.8%) than MOUD (35.1%), or SPDP (24.1%) strategies. Site-adjusted differences were not significant for either hypothesis related to OEND or MOUD strategy selection. HCS communities selected strategies from the ORCCA menu well beyond minimum requirements using a flexible approach to address unique needs. • The HEALing Communities Study (HCS) seeks to reduce overdose deaths in 67 highly impacted communities in Kentucky, Massachustees, New York, and Ohio. • Overdose deaths are reduced by the implementation of evidence-based practices. • The Opioid-overdose Reduction Continuum of Care Approach organizes EBP strategies under three menus: Overdose Education and Naloxone Distribution, Medication Treatment for Opioid Use Disorder, and Safer Prescribing and Dispensing Practices. • This paper describes strategies selected by communities and whether selection of overdose education and naloxone distribution differes by opioid overdose deaths and whether medicaiton for opioid use disorder differs between urban and rural settings. [ABSTRACT FROM AUTHOR]

On March 22, 2020, the State of New York issued a "stay-at-home" policy, wherein all non-essential businesses were on pause until June 8, 2020. The bike-sharing system (BSS) and yellow taxi system (YTS) in Manhattan were substantially affected. This sudden drop in demand can impact not only short and long-term mobility but also the sustainability of transport network. Given that few empirical studies are focusing on the impacts of the "stay-at-home" policy on the BSS and YTS, this further substantiates the importance of analyzing how the policy affects the overall transportation system in New York City (NYC). This paper aims to fill this gap by quantifying the impacts of the "stay-at-home" policy on the two aforementioned transportation systems. Specifically, the following three research gaps are summarized in this study: I) The hidden biases in current "stay-at-home" policy estimation methods were not properly addressed; II) The policy impacts on BSS and YTS during different periods of the effective day were unclear; III) The sensitivity of uncontrolled confounders in long-term policy impact estimations was poorly discussed. We addressed these important research gaps by introducing robust statistical approaches like regression discontinuity design (RDD) and propensity score matching (PSM) methods, which can overcome methodological challenges such as counterfactual restoration, spatiotemporal heterogeneities, and unmeasured confounders. The BSS and YTS were studied at the aggregated neighborhood levels. Results demonstrate that the impacts on BSS have higher variations than YTS usage. The monthly average treatment effects on the treated (ATT) for BSS ranged from −72% to −28% respectively in March and June, while YTS ranged from −96% to −94%. Evidence suggests that demand for BSS surged on weekends in May and June. Understanding the impact of this short-term yet significant policy change on travel behavior will help optimize supply and demand management strategies, thereby improving the long-term sustainability should similar situations arise in the future. • Quantifying the short-term and long-term impacts of the "stay-at-home" policy. • Data-driven approaches using regression discontinuity design and propensity score matching method. • The bike-sharing system and yellow taxi system have different sensitivities to the policy. • Policy implications are derived from the robust analyses. [ABSTRACT FROM AUTHOR]

• NYSolarCast is an open-source solar forecasting system configurable for any region. • NYSolarCast predicts gridded GHI and both utility-scale and distributed PV power. • NYSolarCast integrates NWP models, machine learning, and real-time weather data. • NYSolarCast was validated for 1 year of 0–6-h GHI and power forecasts in New York. • NYSolarCast GHI forecasts performed comparably to a commercial reference forecast. Solar energy generation capacity will need to be greatly increased to meet aggressive clean energy targets by New York State (NYS), which are 70% renewable energy (RE) generation by 2030, and 100% by 2040. Because solar energy is a variable, weather-driven resource, accurate forecasts of solar energy generation both in nowcast (intra-day) and day-ahead time horizons are necessary for electric utilities and independent system operators, as they maintain grid stability and maximize RE use. Meeting this need for NYS, a gridded, open-source solar power forecasting system called NYSolarCast was developed (https://github.com/ncar/NYSolarCast%5fdelivery). NYSolarCast makes 15-min resolution predictions of global horizontal irradiance (GHI) on a 3-km grid covering all of NYS, which are then used to estimate solar power generation both for select utility-scale PV plants (15-min resolution) and for zone-aggregated distributed PV (1-h resolution). The statewide GHI forecasts are made by applying the StatCast statistical forecasting model, which is trained on over two years of GHI observations from pyranometers at all 126 NYS Mesonet stations and gridded numerical weather prediction forecasts from both the Weather Research and Forecasting model tuned for solar applications (WRF-Solar®) and NOAA's operational High-Resolution Rapid Refresh (HRRR) model. Forecasts are made at each NYS Mesonet site and then blended outward into the rest of the grid. This paper gives an overview of NYSolarCast performance for intra-day (0–6-h) GHI and power forecasts during a one-year period. [ABSTRACT FROM AUTHOR]

The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas (GHG) emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing and Charging (JRC) scheduling for electric trucks. The objective of the associated optimization problem is to minimize the cost of transportation, charging, and tardiness. A large number of possible combinations of road segments as well as a large number of combinations of charging decisions and charging durations leads to a combinatorial explosion in the possible decisions electric trucks can make. The resulting mixed-integer linear programming (MILP) problem is thus extremely challenging because of the combinatorial complexity even in the deterministic case. Therefore, a Surrogate Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the overall problem into significantly less complex truck subproblems. In the coordination aspect, each truck subproblem is solved independently of other subproblems based on the values of Lagrangian multipliers. In addition to serving as a means of guiding and coordinating trucks, multipliers can also serve as a basis for transparent and explanatory decision-making by trucks. Testing results demonstrate that even small instances cannot be solved using the off-the-shelf solver CPLEX after several days of solving. The SLBLR method, on the other hand, can obtain near-optimal solutions within a few minutes for small cases, and within 30 min for large ones. Furthermore, it has been demonstrated that as battery capacity increases, the total cost decreases significantly; moreover, as the charging power increases, the number of trucks required decreases as well. • Present a formulation of a joint heavy-duty vehicle fleet routing & charging problem. • Use surrogate level-based Lagrangian relaxation approach to solve the problem. • The proposed method obtains near-optimal solutions for realistic testing cases. [ABSTRACT FROM AUTHOR]

Neighborhood socioeconomic status (SES) and greenspace can affect respiratory health. However, it is unclear whether effects of neighborhood SES and greenspace on respiratory health still exist regardless of temperature variations. This paper conducted a two-stage, age-stratified case time-series study. The first goal is to examine the associations between two temperature metrics (daily mean temperature [DMT] and diurnal temperature range [DTR]) and respiratory emergency department (ED) visits among four age groups in New York City. The second goal is to evaluate whether neighborhood SES and greenspace would be determinants of respiratory ED visits independent from temperature varying factors. A distributed lag nonlinear model was applied on ED data from 135 zip codes (October 2016 - February 2020). Our first-stage analysis indicated that older adults aged 65+ had higher risk of ED visits (RR=2.78, 95% eCI: 2.41, 3.22; with 7 days of lag) on days with low DMT (-10 °C), followed by adults aged 18-64 (RR=2.48, 95% eCI: 2.32, 2.65), children and youth aged 5-17 (RR=1.38, 95% eCI: 1.24, 1.53), and young children aged 0-4 (RR=1.04, 95% eCI: 0.96, 1.13). However, no excess respiratory ED visits were observed on days with high DMT (30 °C). Higher DTR was associated with higher risk, with children and youth more susceptible when DTR was high (DTR 20 °C; RR=5.70, 95% eCI: 3.42, 9.49; with 7 days of lag). The second-stage analysis indicated neighborhood SES and greenspace had significant associations with respiratory ED visits regardless of temperature variations. Specifically, Higher income and greenspace exposure were negatively associated with ED visits among all age groups. Neighborhood SES and greenspace could affect respiratory morbidity regardless of weather conditions. Daily temperature variations accelerated the short-term risk among population subgroups under different weather conditions (e.g., higher risk of days with low DMT among older adults, higher risk of days with high DTR among children and youth aged 5-17), which could create co-effects with neighborhood SES and greenspace on respiratory health. • Respiratory ED visit was significantly associated with both DMT and DTR. • Older adults more vulnerable to low DMT (RR=2.78, 95% eCI: 2.41, 3.22). • Youth aged 5-17 more vulnerable to high DTR (RR=5.70, 95% eCI: 3.42, 9.49). • SES and greenspace associated with respiratory ED visit regardless of temperature. • Intracity examination of impacts of DMT and DTR on health allows local actions. [ABSTRACT FROM AUTHOR]

• Galleries in New York had always played important roles in NY art world. • The boundaries between NY museums and other organizations gradually collapsed. • Boundaries had been formed between the university and other organizations. • NY galleries and museums in other American cities had strong connections in the 1940s. • The connection between NY galleries and NY museums became more apparent in the 1960s. This paper examines the composition and transition of symbolic boundaries in the New York art world of painting and sculpture from 1940 to 1969 through social network analysis of inter-organizational ties between art venues. As culture-led redevelopments become more controversial in the age of neoliberal urban management, recent studies make it clear that symbolic boundaries and boundary works of urban cultures become more important topics for urban studies. So far, existing research on symbolic boundaries in urban culture has focused on social class and urban organizations, such as amenities and business establishments, and have pointed to the loss of the symbolic boundary between the museum and the gallery, this paper will show that more diverse changes were occurring in addition to such changes while following the critical methods of the previous studies. Utilizing the exhibition histories of 43 prominent American avant-garde artists based in New York, I obtain affiliation network data of artists-by-art venues. Based on the affiliation data, this paper examines the social networks among art venues, such as museums, galleries and universities, using block modeling and other methods of social network analysis. [ABSTRACT FROM AUTHOR]

Background: The New South Wales (NSW) Heart Failure Snapshot sought to provide a contemporaneous profile of patients admitted with acute heart failure. We have previously reported the baseline results, and this paper reports the 30-day and 12-month outcomes.Methods: A prospective audit of consecutive patients admitted to 24 teaching hospitals across NSW and the Australian Capital Territory in July-August 2013 with acute heart failure. Follow-up data were obtained by integration of hospital administrative records and follow-up phone calls with the patients.Results: Eight hundred eleven (811) patients were recruited across the 24 sites. The NSW HF Snapshot was an elderly cohort (77 ± 14 yrs) with high comorbidity (mean Charlson Comorbidity Index 3.5 ± 2.6), and 71% were frail at baseline. Twenty-four per cent (24%) of patients were readmitted within 30-days post discharge. One hundred seventy-eight (178) patients died within 12 months post discharge. The independent predictors of death were frailty (Hazard Ratio 1.98 [95% Confidence interval 1.18-3.30]; p < 0.01) Charlson Comorbidity Index (HR 1.06 [95% CI 1.00-1.13]; p = 0.05); New York Heart Association (NYHA) class 4 (HR 2.62 [95% CI 1.32-5.22]; p < 0.01); eGFR<30 ml/min/1.73 m2 (HR 2.16 [95% CI 1.45-3.21]; p < 0.01); hypokalaemia at discharge (HR 2.55 [95% CI 1.44-4.51]; p < 0.01) and readmission within 30 days of baseline admission (HR 2.13 [95% CI 1.49-3.13]; p < 0.01).Conclusion: In one of the largest prospective audits of acute heart failure outcomes in Australia, we found that short-term readmissions and mortality at 12 months remain high but were largely driven by patient-level factors. [ABSTRACT FROM AUTHOR]

• Quantitative study of the capacity of communities in New York State to provide registered childcare slots for age-eligible infants and toddlers. • Builds on the growing literature documenting the slow decline of registered child care providers and slots. • Advances the literature on child care deserts. • Advances our understanding of the negative relationship between the capacity of non-urban (especially rural) communities and PreK programming. Access to high quality child care provides a valuable support to families and communities as it both enables parental employment and provides a safe and developmentally appropriate place for children. Yet, access to child care for children under age three is uneven, with some communities experiencing child care "deserts" (Jessen-Howard et al., 2018). This paper assesses the infant and toddler child care capacity – the proportion of age-eligible children for which there are available child care slots – of communities across New York State. Using data from the NY State Education Department (670 school districts in the most recent year of available data), the NYS Office of Children and Family Services (about 18,000 registered child care providers), and the National Center for Education Statistics definitions of locale, we examine how child care capacity relates to school district and community factors over 9 years (2007–2016). Using negative binomial regression, we see overall stability in capacity for infant and toddler child care in New York State, but important variation by demographic factors and geography. Non-urban communities and communities with greater levels of student poverty have less capacity for infants and toddlers. Urban communities, communities with publicly funded prekindergarten and greater levels of minority children are associated with greater capacity. Importantly, in non-urban communities only (and especially in rural communities), we find an interaction effect that documents significantly reduced capacity for infant and toddler child care in communities also implementing public prekindergarten. [ABSTRACT FROM AUTHOR]

Air pollution has a significant impact on human health and influences housing choices. Existing studies on determinant factors affecting property prices, including air pollution, mainly employed hedonic and spatial regression models that have limitations in capturing non-linear relationships and local interactions. Neglecting the non-linear relationships can lead to incomplete estimation impacts between explainable features and prices. To fill these gaps, this study used machine learning algorithms and SHAP (SHapley Additive exPlanations) to identify the relative importance of air pollutant variables on land values and their non-linear mechanism. The results showed that the Extreme Gradient Boosting (XGBoost) outperformed Ordinary Least Squares (OLS), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), K-Nearest Neighbors (K-NN), and Extra Tree Regression (ETR) models in price prediction and capturing the non-linear relationship between air pollution and land values. Notably, Ozone (O 3) and Nitric Oxide (NO) concentrations contribute 1.64% and 1.47% to land value variation, respectively. The results confirmed the non-linear relationship and identified that the threshold effect between air pollution and land values is at mean concentration values. Furthermore, we observed that the negative influence of O 3 and NO on land values appears as their concentration level is higher than 29.3 and 13 ppb, respectively. This paper contributes valuable insights by advancing our understanding of the non-linear mechanism impact of air pollution on house and land values, informing environmental policymaking, and shedding light on housing decisions. [ABSTRACT FROM AUTHOR]

• COVID-19 travel-restriction policies can affect road traffic accident patterns. • NYC data shows a decrease in number of accidents but an increase in severity. • There has been a sharp increase in severe accidents involving cyclists. • Bike sharing trips can help predict safety risks for cyclists across space and time. • Hot zones for severe cyclist accidents are spatially and temporally concentrated. Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists' safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists' severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered. [ABSTRACT FROM AUTHOR]

• We analyze the resilience of weighted networks. • We propose a measure of resilience based on shock propagation along network paths. • We introduce a condition for shock propagation based on the weights of the network. • We test the theoretical proposal on two real-world instances of air traffic systems. Networks are at the core of modeling many engineering contexts, mainly in the case of infrastructures and communication systems. The resilience of a network, which is the property of the system capable of absorbing external shocks, is then of paramount relevance in the applications. This paper deals with this topic by advancing a theoretical proposal for measuring the resilience of a network. The proposal is based on the study of the shocks propagation along the patterns of connections among nodes. The theoretical model is tested on the real-world instances of two important airport systems in the US air traffic network: Illinois (including the hub of Chicago) and New York states (with JFK airport). [ABSTRACT FROM AUTHOR]

Background: The problem of injection drug use in public bathrooms has been documented from the perspectives of people who inject drugs and service industry employees (SIEs). Previous studies suggest that SIEs are unaware of how to respond to opioid overdoses, yet there are no behavioral interventions designed for SIEs to address their specific needs. In response to this gap in the field, we constructed, implemented, and evaluated a three-module behavioral intervention for SIEs grounded in the Information-Motivation-Behavioral skills model. This paper focuses on the evaluation of one module, namely, the intervention component addressing overdose response and naloxone administration (ORNA).Methods: Participants were SIEs (N = 18 from two separate business establishments) recruited using convenience sampling. The study utilized a pre-/post-test concurrent nested mixed method design and collected quantitative and qualitative data including an evaluation of the intervention module. The primary outcomes were opioid overdose-related knowledge and attitudes. Acceptability was also assessed.Results: SIEs demonstrated significant improvements (p <  0.01, Cohen's d = 1.45) in opioid overdose-related knowledge as well as more positive opioid overdose-related attitudes (p< 0.01, Cohen's d = 2.45) following the intervention. Participants also reported high levels of acceptability of the module and suggestions for improvement (i.e., more role-playing).Conclusions: This study highlights the acceptability and evidence of efficacy of the ORNA module, as well as the utility of training SIEs in ORNA. The expansion of this training to other SIEs and public employees (librarians, etc.) who manage public bathrooms warrants further investigation. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of spatio-temporal factors on human comfort was quantified. • Seasonal change is the main factor affecting physical comfort. • The artificial impermeable matrix increases the surface thermal field temperature. • Natural factors can always effectively reduce the surface temperature. • Human comfort can be improved to a certain extent through human intervention. In recent decades, urbanization and the dramatic increase in urban populations have exacerbated the urban heat island effect. At present, much attention has been paid to the causes and patterns of the urban heat island effect. However, there are few quantitative studies on the impact of the urban heat island effect. By extracting post-world climate data, DEM data, and land use data for 20 years between 2001 and 2020, this paper first studies the variation pattern and spatial distribution characteristics of urban heat islands in New York State, summarizes the seasonal distribution characteristics of temperature, and then uses Giles formula to calculate Tom's discomfort index and evaluate human thermal comfort, and evaluates the effect of heat island effect on human sensory thermal comfort. The results show that on the time scale, the surface temperature in the study area generally showed a slow upward trend over the past 20 years. For example, in July, the maximum and minimum temperatures increased by 3.2 °C and 4.1 °C, respectively. At the spatial scale, most of the heat island areas in the study area were distributed in the New York City agglomeration, especially from May to October, when the heat island effect was particularly obvious. The temperature map showed obvious high temperatures. Compared with 2001, the human discomfort index (DI) increased between June and August 2020. The land use map shows that as the city expands, people's DI index also increases, and the proportion of people who feel uncomfortable with heat increases to 50%. Except for the New York City cluster, other areas were mostly athermic-free. This result shows that excessive urban development concentration seriously affects residents' quality of life. We should pay attention to the superimposed impact of climate change and urban heat islands on the human discomfort index, and adjust the local high temperature and thermal field area through reasonable planning, strengthening greening, and using building technology to make cities more livable. [ABSTRACT FROM AUTHOR]

Social determinants of health may impact stage and aggressiveness of cancer presentation. As neighborhoods may influence adjacent neighborhoods across often porous and arbitrary borders, an analysis that measures the spatial impact of these social determinants provides a more holistic evaluation than an analysis that treats each neighborhood as independent and unrelated units. In this paper, we study the impact of sociodemographic and environmental characteristics of neighborhoods on cancer presentation in New York City, using both patient-level analysis and spatial autoregression on the New York State Public Access Cancer Epidemiology Data (NYSPACED) and the NYC Open neighborhood-level dataset. Two outcomes of interest were chosen: Presentation with high grade prostate cancer and presentation with distant disease. We performed logistic regression on individual patient level analysis. We created generalized cross sectional autoregressive spatial regression models to assess direct and indirect (via adjacent neighborhoods) impact of neighborhood level independent variables. We analyzed the following neighborhood level variables: 1) Percent population that was Black, 2) Percent population living in poverty, 3) Percent population obese based on self-reported height and weight, 4) Percent who report being current smokers, 5) Percent who report eating one or more servings of fruits and/or vegetables in the last day, and 6) annual average micrograms of fine particulate matter per cubic meter of air. We identified 95,749 patients diagnosed with prostate adenocarcinoma in New York City from 2001-2018. With reported Black race as the referent value, White patients were less likely to present with high grade disease (OR 0.92 [95% CI 0.89-0.95]). Similarly, self-identified White patients were also less likely to present with distant disease (vs. Black, OR 0.76 [95% CI 0.72-0.81]). Spatial autoregression models revealed no neighborhood level variables associated with direct or indirect impacts on presentation with high grade disease. However, there were several variables initially associated with distant disease presentation, including race, poverty, obesity, fruit and vegetable consumption, and air pollution. However, only neighborhood poverty and obesity levels continued to have significant direct impact on presentation with distant disease when included in the regression model with percent Black residents. In conclusion, we discovered a strong association between Race and poverty with the likelihood of presenting with advanced prostate cancer, at both the individual and neighborhood levels. Geographically targeted outreach to improve prostate cancer screening and access to care should be focused on Black men and neighborhoods with high levels of poverty. [ABSTRACT FROM AUTHOR]

• Detecting outliers (anomalies) is critical in safety-related applications like ATM. • Metroplex airspace is most congested and highly-coordinated of all components of ATM. • ATM data capturing technologies enables data-driven approach to improving safety and throughput. • The anomaly detection algo must be interpretable, and it must evolve with and adapt to airspace. • This is best achieved through ingesting both operational, and real-time surveillance data. The airspace system is a complex dynamical system with complicated controlled interactions between its constituent subsystems – terminal airspace, en-route airspace, and ground. Of these, air traffic management in the multi-airport (metroplex) terminal airspace is one of the most complicated subsystems to manage, especially due to the interactions between proximal airports. Analyzing anomalous behaviors in the metroplex is emerging as a key problem in understanding air traffic management complexity and safety. Although physics-based approaches have been studied in-depth for this application, newfound interest has been observed to use recorded time-series air traffic surveillance and airport operations datasets for this purpose. In this paper, we propose a machine learning-based anomaly detection algorithm that generates mathematical models to detect anomalies in metroplex operations. Several machine learning algorithms have been developed to detect anomalies using only air traffic surveillance data, but there is a significant scope of improvement by including airport operational characteristics as well, since integrating such closely-controlled metroplex operational datasets allows the developed models to effectively detect anomalies. The key contribution of this paper is in allowing anomaly detection models to recursively update so as to adapt to changes in metroplex operations. The proposed algorithm is demonstrated with real air traffic surveillance and airport operations datasets at LaGuardia, John F. Kennedy, and Newark airports, thereby detecting anomalies for operations in the New York metroplex. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once. • This paper presents a multi-objective CA land use/cover change model. • The model uses a novel multi-objective Markov chain Monte Carlo (MO-MCMC). • We compare MO-MCMC with a multi-objective genetic algorithm (MO-GA). • We apply our model to simulate the expansion of multi-density urban areas in New York. • The results show that the MO-MCMC outperformed MO-GA in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

Hurricane Sandy revealed the higher-risk vulnerability to natural hazards of civil infrastructure systems in coastal megacities such as New York. Traditional sources of funding for both recovering from disasters and preventing future damages are not only limited, but also do not account for benefit transfers of the externalities induced by the provision of resilient infrastructure. In principle, property owners should be willing to pay (WTP) an amount equal to the perceived benefit, if this positive externality is internalized by them following some pricing mechanism. In this paper, we analyze the willingness of residents to financially support improvements in the resilience to extreme events of the transportation system in New York City. Choice microdata was collected for over 1500 residents of the metropolitan NYC area. Several logit-type models were estimated and the preferred model was a discrete-continuous heterogeneity mixture that allows for the derivation of flexible distributions of willingness to pay. Using hypothetical scenarios of recovery, the annual willingness to pay for individuals who missed work ands self identify as politically liberal ranges from $120 to $775, whereas for those individuals that were not directly affected the range is $15-$50. [ABSTRACT FROM AUTHOR]

The wildfires in Fort McMurray of Alberta, Canada, injected large amounts of smoke aerosols in May 2016 and were identified as being one of Canada's major weather events of the year. This paper presents a synergistic remote sensing and in-situ measurement of the resultant smoke plume transport, optical properties, and its impacts on local air quality in New York City (NYC). Comparisons with the operational air quality model forecast (the NOAA National Air Quality Forecasting Capability, NAQFC) performance are presented. The aloft plume intrusions on May 9–13 and 25–29, 2016, and their mixing down into the planetary-boundary layer (PBL) were observed by a combined lidar, ceilometer and other measurement. A decrease in single-scattering albedo and absorption Angstrom exponent near one indicates that the plumes were absorbing aerosol dominated. Dramatic impacts of smoke transport on the ground air quality are demonstrated with a coincident increase of ground PM 2.5 (from 5- to 25–30 μg/m 3 ) in NYC urban and upwind rural area, enhancement of the PM 2.5 speciation (organic carbon, elemental carbon, potassium ion (K + )) and the ozone exceedance of NAAQS. Using the satellite and model product, we show regional spatial distribution of smoke, multiple transport paths and wildfire sources. Finally, with the lidar vertical profiling observations, we evaluate the model PBL-height (PBLH) and PM 2.5 during May 24th to 30th, 2016. The model PBLH shows consistent diurnal variation with the observed mixing layer height (MLH), but is clearly overestimated during the convective daytime hours. On the other hand, when estimating the MLH directly from the model PM 2.5 profile, better agreement with observation was indicated. This helps explain the good agreement between the model PM 2.5 and surface measurements except for the model overestimate during the morning of May 25 and 26, 2016. [ABSTRACT FROM AUTHOR]

Objective Integrating primary care services into specialty mental health clinics has been proposed as a method for improving health care utilization for medical conditions by adults with serious mental illness. This paper examines the impact of a mental health based primary care program on emergency department (ED) visits and hospitalizations. Method The program was implemented in seven New York City outpatient mental health clinics in two waves. Medicaid claims were used to identify patients treated in intervention clinics and a control group of patients treated in otherwise similar clinics in New York City. Impacts of the program were estimated using propensity score adjusted difference-in-differences models on a longitudinally followed cohort. Results Hospital stays for medical conditions increased significantly in intervention clinics relative to control clinics in both waves (ORs = 1.21 (Wave 1) and 1.33 (Wave 2)). ED visits for behavioral health conditions decreased significantly relative to controls in Wave 1 (OR = 0.89), but not in Wave 2. No other significant differences in utilization trends between the intervention and control clinics were found. Conclusion Introducing primary care services into mental health clinics may increase utilization of inpatient services, perhaps due to newly identified unmet medical need in this population. [ABSTRACT FROM AUTHOR]

This paper describes a scenario-based approach for evaluating the cascading impacts of sea level rise (SLR) and coastal flooding on emergency responses. The analysis is applied to Lower Manhattan, New York City, considering FEMA’s 100- and 500-year flood scenarios and New York City Panel on Climate Change (NPCC2)’s high-end SLR projections for the 2050s and 2080s, using the current situation as the baseline scenario. Service areas for different response timeframes (3-, 5- and 8-min) and various traffic conditions are simulated for three major emergency responders (i.e. New York Police Department (NYPD), Fire Department, New York (FDNY) and Emergency Medical Service (EMS)) under normal and flood scenarios. The modelling suggests that coastal flooding together with SLR could result in proportionate but non-linear impacts on emergency services at the city scale, and the performance of operational responses is largely determined by the positioning of emergency facilities and the functioning of traffic networks. Overall, emergency service accessibility to the city is primarily determined by traffic flow speed. However, the situation is expected to be further aggravated during coastal flooding, with is set to increase in frequency and magnitude due to SLR. [ABSTRACT FROM AUTHOR]

In location-based social networks (LBSNs), point-of-interest (POI) recommendation systems help users identify unvisited POIs by filtering large amounts of information. Accurate POI recommendations can effectively improve user satisfaction and save time in finding POIs. In recent years, the graph convolution network (GCN) technique, which enhances the representational ability of neural networks by learning the embeddings of users and items, has been widely adopted in recommendation systems to improve accuracy. Combining GCN with various information, such as time and geographical information, can further improve recommendation performance. However, existing GCN-based techniques simply adopt time information by modeling users' check-in sequences, which is insufficient and ignores users' time-based high-order connectivity. Note that time-based high-order connectivity refers to the relationship between indirect neighbors with similar preferences in the same time slot. In this paper, we propose a new time-aware GCN model to extract rich collaborative signals contained in time information. Our work is the first to divide user check-ins into multiple subgraphs, i.e., time slots, based on time information. We further propose an edge propagation module to adjust edge affiliation, where edges represent check-ins, to propagate user's time-based preference to multiple time slots. The propagation module is based on an unsupervised learning algorithm and does not require additional ground-truth labels. Experimental results confirm that our method outperforms state-of-the-art GCN models in all baselines, improving Recall @ 5 from 0.0803 to 0.0874 (8.84%) on the Gowalla dataset and from 0.0360 to 0.0388 (7.78%) on the New York dataset. The proposed subgraph mining technique and novel edge-based propagation module have high scalability and can be applied to other subgraph construction models. [ABSTRACT FROM AUTHOR]

• Freight carriers can take multiple reactions to toll increases. • The willingness to take the following four reactions were investigated: pass on the costs to receivers; reduce delivery frequency; reduce travel distance; and change the time of day. • The four reactions were positively correlated so that they are complementary to each other. • The type of commodity and market power matter when determining which reactions to take. Freight carriers' behavior greatly varies given the number of parties involved in the process from the origin end to the destination end and the heterogeneity of commodities. Accordingly, their reaction to transportation policies including road pricing is composed of multidimensional choices, e.g., they can take a series of actions in multiple stages in response to policies, which have been overlooked in the literature. To better understand carriers' behavior, this paper investigated freight carriers' behavior in reaction to toll increases in New York State using a stated preference survey. The following four reactions in response to hypothetical toll increase scenarios were jointly investigated: (a) passing on the costs to receivers; (b) reducing delivery frequency; (c) reducing travel distance on toll roads; and (d) changing time of day of travel. The descriptive analysis showed that reducing mileage on toll roads and passing on the costs are the two most preferred reactions while changing delivery time to avoid high toll is the least favorable reaction. A multivariate Probit model was also developed to quantitatively explore factors affecting the willingness to take the reactions. As a result, all the four reactions were positively correlated, suggesting that the reactions selected by carriers are complementary to each other. Factors (including commodity type and company size) affecting each reaction were investigated. Implications for planning and policymaking based on the results were also discussed. [ABSTRACT FROM AUTHOR]

• Thirteen of eighteen dentists obtained positions as university professors in the USA. • New York and Illinois offered the most favorable university environments. • No other destination country offered comparable opportunities for dentist émigrés to thrive academically. • Specialization in sought-after sub-disciplines within dentistry facilitated success. • Austrian dentists benefited from the internationally renowned Vienna School. This study is the first to examine the collective of dental lecturers and scientists who emigrated from Nazi Germany to the United States of America. We pay special attention to the socio-demographic characteristics, emigration journeys, and further professional development of these individuals in the country of immigration. The paper is based on primary sources from various German, Austrian, and United States archives and a systematic evaluation of the secondary literature on the persons concerned. We identified a total of eighteen male emigrants. The majority of these dentists left the "Greater" German Reich between 1938 and 1941. Thirteen of the eighteen lecturers were able to find a position in American academia, mainly as full professors. Two-thirds of them settled in New York and Illinois. The study concludes that most of the emigrated dentists studied here succeeded in continuing or even expanding their academic careers in the USA, although they usually had to retake their final dental examinations. No other destination country for immigration offered similarly favorable conditions. Not a single dentist decided to remigrate after 1945. [ABSTRACT FROM AUTHOR]

This paper seeks to address the profound power resilience inequity in New York City by means of strategic allocation of electric vehicle (EV) charging infrastructure to support the power grid operation in challenging scenarios, such as when facing high demand or during natural disasters. First, we uncover the most disproportionately affected communities in New York by developing a metric of power resilience inequity to measure the combined impact of power failure-related factors on these areas. We employ data-driven approaches to infer the statistical relationships between communities' power resilience index, their available EV charging infrastructure, and several other prominent socio-demographical variables. This inference yields the development of a machine learning model that can predict the reduction of power resilience inequity after deployment of the proposed resource allocation strategy. We further develop an optimization framework that jointly considers equity and efficiency to guide the optimized distribution of EV charging infrastructure across the city. A number of case studies are leveraged to demonstrate the capability of the devised approach in enhancing urban power resilience equity, yielding favorable results in marginalized communities. [ABSTRACT FROM AUTHOR]

Bike-sharing data have been a valuable source for urban transport research. While most studies focus on Origin-Destination (OD) data of users' bike trips in bike-sharing researches, little has been investigated on mobility patterns from the perspective of bikes. Bike mobility can not only reflect human mobility patterns but also provide practical insights for understanding the bike-sharing system and assisting bike management. In particular, continuous bike movement information can provide abundant spatiotemporal connection information for spatial context analysis in data without user ID information. This paper studies bike mobility by shifting the perspective from OD analysis to the new analysis primitive of bike mobility chain (BMC). First, bike-sharing OD trip data are reconstructed to obtain BMCs. Second, the reconstructed BMCs are feature-augmented, based on which the spatiotemporal characteristics of bike mobility are analyzed. Finally, the word embedding model and the clustering algorithm are applied to the BMCs, which embody rich spatiotemporal inter-station connectivity, to facilitate mining stations with similar bike mobility. The methodology is applied for the comparative study of bike-sharing activities on workdays and holidays using data collected from New York Citi-Bike. The experimental results manifest locality patterns of bike mobility in both mobility chain indicators and multi-scale station embedding analysis results, and reveal disparities in BMC statistic indicators and spatial distribution mode of stations with similar bike mobility. These results can provide a new empirical reference for urban bike management in different time periods. As a more general implication, this study broadens the perspective for OD-based data analysis and paves the path to leverage the thriving trajectory-based research for further investigations. [ABSTRACT FROM AUTHOR]

We respond to the paper "Carbon emissions and costs associated with subsidizing New York nuclear instead of replacing it with renewables" (Cebulla and Jacobson, 2018). The paper found that it would be cheaper to build new renewable energy infrastructure than to continue to subsidise existing nuclear power plants via Zero Emission Credits. We identify a possible error in the calculation of the total annual energy produced by these power plants that, if corrected, could invert the paper's central finding regarding cost. • Cited comparison of nuclear and renewables in New York State appears not to account for all relevant nuclear production. • Continued nuclear subsidy overall cheaper than new renewable replacement. • Estimate of carbon savings relies on high estimate of nuclear's carbon emissions. [ABSTRACT FROM AUTHOR]

• Collection of papers introducing design and implementation of HEALing Communities Study. • Overview of how to reduce opioid overdose deaths nationally and locally. • Describes the diversity and burden of overdose in HCS communities. The severity of the overdose epidemic underscores the urgent need for innovative and high impact interventions that promote the rapid penetration and scale up of evidence-based practices (EBPs) in communities profoundly affected by fatal opioid overdose. This special issue shares scientific advancements in implementation research design and evaluation of a novel data-driven community-based intervention. The HEALing (Helping End Addiction Long-Term) Communities Study (HCS) is a four-year study that is designed to examine the effectiveness of the Communities That HEAL (CTH) intervention. The CTH intervention supports the dissemination of EBPs in 67 communities across four high-burdened states—Kentucky, Massachusetts, New York, and Ohio. The diversity in these communities in terms of rural-urban status, race-ethnicity and other social determinants of health facilitates generalizability of results to other communities across the US. The nine papers in this special issue describe critical elements that constitute the HCS framework and design. This includes the implementation of EBPs that have a substantial impact on fatal and non-fatal opioid overdose, the Opioid-overdose Reduction Continuum of Care Approach, communication campaigns to increase awareness and demand for EBPs and reduce stigma against people with OUD and MOUD interventions, and the process of community engagement. This includes how to form community coalitions and gain their commitment, and steps taken to mobilize coalitions to pursue EBP implementation and ensure EBPs are adapted for community needs. The collective papers in this issue demonstrate that the design of any complex study must adapt to unanticipated temporal events, including the rapidly emerging COVID-19 crisis. Readers will learn about the scientific process of the design and implementation of a community-engaged intervention, its methodologies, guiding conceptual models, and research implementation strategies that can be applied to address other health issues. [ABSTRACT FROM AUTHOR]

This paper uses the random-coefficients logit methodology that controls for potential endogeneity of prices and allows for general substitution patterns to estimate various demand systems. The estimation takes advantage of an original ticket-level revealed preference data set on travels from the New York City area to Toronto that contains prices and characteristics of not only flight choices but also of all non-booked alternative flights. Consistent with having higher valuations, our results show that travelers buying closer to departure have a higher utility of flying. Moreover, consumers’ heterogeneity decreases as the flight date nears. At the carrier level, we identify which carriers have more price-sensitive consumers and which carriers face greater competition. In addition, the results suggest that our multi-airport metropolitan area can be considered as a single market and that JFK and Newark are relatively closer substitutes. Overall, consumers are more willing to switch to alternative carriers than between airports or departure times. [ABSTRACT FROM AUTHOR]

Urban water and energy systems are crucial for sustainably meeting basic service demands in cities. This paper proposes and applies a technology-independent “reference resource-to-service system” framework for concurrent evaluation of urban water and energy system interventions and their ‘nexus’ or ‘interlinkages’. In a concrete application, data that approximate New York City conditions are used to evaluate a limited set of interventions in the residential sector, spanning from low-flow toilet shifts to extensive green roof installations. Results indicate that interventions motivated primarily by water management goals can considerably reduce energy use and contribute to mitigation of greenhouse gas emissions. Similarly, energy efficiency interventions can considerably reduce water use in addition to lowering emissions. However, interventions yielding the greatest reductions in energy use and emissions are not necessarily the most water conserving ones, and vice versa. Useful further research, expanding the present analysis should consider a broader set of resource interactions, towards a full climate, land, energy and water (CLEW) nexus approach. Overall, assessing the impacts, trade-offs and co-benefits from interventions in one urban resource system on others also holds promise as support for increased resource efficiency through integrated decision making. [ABSTRACT FROM AUTHOR]

This paper investigates the differences between single-vehicle and multi-vehicle truck crashes in New York City. The random parameter models take into account the time of day effect, the heterogeneous truck weight effect and other influencing factors such as crash characteristics, driver and vehicle characteristics, built environment factors and traffic volume attributes. Based on the results from the co-location quotient analysis, a spatial generalized ordered probit model is further developed to investigate the potential spatial dependency among single-vehicle truck crashes. The sample is drawn from the state maintained incident data, the publicly available Smart Location Data, and the BEST Practices Model (BPM) data from 2008 to 2012. The result shows that there exists a substantial difference between factors influencing single-vehicle and multi-vehicle truck crash severity. It also suggests that heterogeneity does exist in the truck weight, and it behaves differently in single-vehicle and multi-vehicle truck crashes. Furthermore, individual truck crashes are proved to be spatially dependent events for both single and multi-vehicle crashes. Last but not least, significant time of day effects were found for PM and night time slots, crashes that occurred in the afternoons and at nights were less severe in single-vehicle crashes, but more severe in multi-vehicle crashes. [ABSTRACT FROM AUTHOR]

Transactive energy systems use dynamic electricity markets to present an improved way to control large numbers of distributed energy resources while maintaining user privacy, autonomy and scalability. However, this access to dynamic electricity prices can introduce negative emergent behavior like high peak loads if not adequately regulated by grid operators. To solve this, this paper proposes a regulated peer-to-peer market structure for residential prosumers. Prosumers bid energy demand or supply curves into a peer-to-peer market, which can then be directly used by the utility to quantify and dispatch demand flexibility. A utility surcharge mechanism can then be employed to dispatch this flexibility, and achieve significant improvements to reliability. This framework is tested in a simulation environment of electrified residential buildings with significantly enhanced fidelity representing the real-world housing stock. New York State and the month of January was chosen for the demonstration purpose, considering that January is one of the most challenging months for fully electrified systems in cold climates as solar production occurs at opposite times of the highest heating load. Results show that the surcharge can reducing peak load and ramp rates by nearly 50%, while also encouraging consumption of locally generated electricity. • Unregulated peer-to-peer electricity markets neglect grid network constraints. • These markets can be regulated by a surcharge imposed by the grid operator. • This surcharge dispatches demand flexibility in a predictable, quantifiable manner. • Decentralized demand flexibility is then quantified by aggregating demand bid curves. • This regulation method is decentralized, scalable, and privacy-preserving. [ABSTRACT FROM AUTHOR]

In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio's value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, which is solved using a multi-objective genetic algorithm. To show the performance of this procedure, we use a data set of asset prices from the New York Stock Market. • The portfolio's value time series implicitly includes correlations among the assets. • Expected return and risk of a portfolio are derived by predicting its future value. • Efficient portfolios are provided by a multi-objective genetic algorithm. [ABSTRACT FROM AUTHOR]

Traffic prediction (forecasting) is a key problem in intelligent transportation. It helps engineers to obtain traffic trends in advance so that they can make favorable decisions quickly and accurately, or even improve the working mode of existing systems. However, it is very challenging to design a model for such problem that fully utilize the factors related to traffic. This paper investigates machine learning in traffic prediction and proposes Multiple Information Spatial–Temporal Attention based Graph Convolution Networks (MISTAGCN). The model consists of two parts. The first part utilizes combinations of different inputs and graph structures to compute the corresponding latent variables. In the second part of the model, the latent variables are comprehensively integrated and deeply mined. In particular, as the basic component of the model, a STBlock integrates mechanisms such as temporal attention, spatial attention, graph convolution, ordinary convolution and residual connections to fully explore the potential information contained in the data. Experiments on Haikou online car-hailing dataset and New York yellow taxi trip dataset illustrate that the proposed model outperforms state-of-art baseline models. • A novel deep learning model for multi–steps traffic prediction problems is proposed. • A unified STBlock structure is devised to process multi channel information. • The same STBlock structure is utilized for information fusion. • Novel formulas are devised to calculate spatial and temporal attentions. • Extensive experiments are conducted to verify the competitiveness of this model. [ABSTRACT FROM AUTHOR]

Over the past several years, police reformers in the United States have tried to develop alternatives to arrest and encourage police to use them more widely, but this work has moved forward with little awareness of similar efforts in the past. This paper provides an overview of one important strand of that history. Drawing on original archival research, it reconstructs the origins of the summons in lieu of arrest in early-20th century New York, and it chronicles the subsequent spread and transformation of this new form of police authority throughout the country. I argue that this story challenges the dominant understanding of the character of 20th century police reform. Where most historians have argued that early 20th century reforms aimed to focus police attention more narrowly on crime control, the police summons arose as an important tool to accommodate the broad array of tasks that American society increasingly expected the police to handle. Where most historians have argued that Progressive-era police reform was driven by an emerging corps of national policing experts who aimed to brush away the dysfunctional practices of the past, the development of the summons emerged from an intensely local process of experimentation that tried to understand and build on the foundation of existing practices. In these respects, the development and spread of the summons provides a distinctive image of the forms that police reform has taken in the past, as well as the forms it might potentially take in the future. [ABSTRACT FROM AUTHOR]

This paper examines first-generation Korean immigrants' barriers to healthcare in the US and their strategies for coping with these issues by analyzing survey data from 507 Korean immigrants and in-depth interviews with 120 Korean immigrants in the New York-New Jersey area. It reports that more than half of Korean immigrants have barriers to healthcare in the US, with the language barrier being the most frequent response, followed by having no health insurance. Korean immigrants are not passive, but rather active entities who display coping strategies for these barriers, such as seeing co-ethnic doctors in the US, seeking Hanbang (traditional Korean medicine) in the US, and taking medical tours to the home country. However, their coping strategies are far removed from formal US healthcare as their behaviors are still restricted to the informal healthcare within the ethnic community or home country. This study methodologically and theoretically contributes to the literature on immigrants’ healthcare behaviors by using a mixed-method approach and developing a specific framework for one particular immigrant group. [ABSTRACT FROM AUTHOR]